Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis.

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

A Multistep, Multicomponent Extraction and Separation Microfluidic Route to Recycle Water-Miscible Ionic Liquid Solvents.

Recycling ionic liquid (IL) solvents can reduce the lifecycle cost of these expensive solvents. Liquid-liquid extraction is the most straightforward approach to purify IL solvents and is typically performed with an immiscible washing agent (e.g., water). Herein, we describe a recycling route for water-miscible ILs in which direct recycling is usually challenging. We use hydrophobic ILs as accommodating agents to draw the water-miscible IL from the aqueous washing stream. A biphasic slug flow of the mixed ILs and water is then separated by using a membrane. The water-miscible IL can then be drawn out from the mixed IL phase with acidified water and dried under vacuum. Both the water-miscible IL and the accommodating agent are then recycled. Here, we demonstrated a proof-of-concept of this process by recycling 1-butyl-3-methylimidazolium trifluoromethanesulfonate (BMIM-OTf) in the presence of the accommodating agent 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BMIM-NTf2) and acidified water. We then demonstrated the capacity to recycle 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) from a realistic synthetic application: Pt nanoparticle synthesis in the water-miscible IL.

Activating Molybdenum Carbide Nanoparticle Catalysts under Mild Conditions Using Thermally Labile Ligands.

Transition-metal carbides are promising low-cost materials for various catalytic transformations due to their multifunctionality and noble-metal-like behavior. Nanostructuring transition-metal carbides offers advantages resulting from the large surface-area-to-volume ratios inherent in colloidal nanoparticle catalysts; however, a barrier for their utilization is removal of the long-chain aliphatic ligands on their surface to access active sites. Annealing procedures to remove these ligands require temperatures greater than the catalyst synthesis and catalytic reaction temperatures and may further result in coking or particle sintering that can reduce catalytic performance. One way to circumvent this problem is by replacing the long-chain aliphatic ligands with smaller ligands that can be easily removed through low-temperature thermolytic decomposition. Here, we present the exchange of native oleylamine ligands on colloidal α-MoC1-x nanoparticles for thermally labile tert-butylamine ligands. Analyses of the ligand exchange reaction by solution 1H NMR spectroscopy, FT-IR spectroscopy, and thermogravimetric analysis-mass spectrometry (TGA-MS) confirm the displacement of 60% of the native oleylamine ligands for the thermally labile tert-butylamine, which can be removed with a mild activation step at 250 °C. Catalytic site densities were determined by carbon monoxide (CO) chemisorption, demonstrating that the mild thermal treatment at 250 °C activates ca. 25% of the total binding sites, while the native oleylamine-terminated MoC1-x nanoparticles showed no available surface binding sites after this low-temperature treatment. The mild pretreatment at 250 °C also shows distinctly different initial activities and postinduction period selectivities in the CO2 hydrogenation reaction for the ligand exchanged MoC1-x nanoparticle catalysts and the as-prepared material.

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis.

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42 650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. A classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

Self-optimizing parallel millifluidic reactor for scaling nanoparticle synthesis.

We developed a 16-channel millifluidic reactor that uses a multiphase gas-liquid flow to continuously produce colloidal CsPbBr3 quantum dots with a throughtput of ∼1 L h-1. The optical properties of the product were monitored, and the reaction conditions were optimized in real time based on the in situ photoluminescence characteristics of the quantum dots.

Continuous Flow Methods of Fabricating Catalytically Active Metal Nanoparticles.

One of the obstacles preventing the commercialization of colloidal nanoparticle catalysts is the difficulty in fabricating these materials at scale while maintaining a high level of control over their resulting morphologies, and ultimately, their properties. Translation of batch-scale solution nanoparticle syntheses to continuous flow reactors has been identified as one method to address the scaling issue. The superior heat and mass transport afforded by the high surface-area-to-volume ratios of micro- and millifluidic channels allows for high control over reaction conditions and oftentimes results in decreased reaction times, higher yields, and/or more monodisperse size distributions compared to an analogous batch reaction. Furthermore, continuous flow reactors are automatable and have environmental health and safety benefits, making them practical for commercialization. Herein, a discussion of continuous flow methods, reactor design, and potential challenges is presented. A thorough account of the implementation of these technologies for the fabrication of catalytically active metal nanoparticles is reviewed for hydrogenation, electrocatalysis, and oxidation reactions.